MMR-and-RMR-workflow-Clutch49
Elliott Schmidt
2024-08-16
Load libraries (packages)
System1 - Dell
Importing data from firesting for resting
preexperiment_date <- "29 June 2023 10 26AM/All"
postexperiment_date <- "29 June 2023 03 23PM/All"
##--- last fish run in trial ---##
experiment_date <- "29 June 2023 12 12PM/Oxygen"
experiment_date2 <- "29 June 2023 12 12PM/All"
firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) System2 - Asus
Importing data from firesting for resting
preexperiment_date_asus <- "29 June 2023 10 49AM/All"
postexperiment_date_asus <- "29 June 2023 04 56PM/All"
##--- last fish run in trial ---##
experiment_date_asus <- "29 June 2023 01 08PM/Oxygen"
experiment_date2_asus <- "29 June 2023 01 08PM/All"
firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) Chamber volumes
chamber1_dell = 0.04650#+0.00022
chamber2_dell = 0.04593#+0.00022
chamber3_dell = 0.04977#+0.00022
chamber4_dell = 0.04860#+0.00022
chamber1_asus = 0.04565
chamber2_asus = 0.04573#+0.00385
chamber3_asus = 0.04551#+0.00322
chamber4_asus = 0.04791#+0.00277
Date_tested="2023-06-29"
Clutch = "131"
Male = "CPRE447"
Female = "CPRE452"
Population = "Pretty patches"
Tank =152
salinity =37
Date_analysed = Sys.Date() Replicates
1
Enter specimen data
Replicate = 1
mass = 0.0004318
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 June 2023 11 43AM/Oxygen"
experiment_mmr_date2 <- "29 June 2023 11 43AM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.00110541post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0006413093Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 8.57
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 3 4 5 6 20 22 47 48 50 51 53 54 55 56 61 62 71 72 74 75
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 8.57
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 3 rate(s) removed, 18 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 12 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 10 1 266.6172 -0.01464672 0.963 NA 4876 5131 11310.35
## 2: 14 1 287.9925 -0.01392057 0.966 NA 7036 7286 13470.35
## 3: 15 1 283.4811 -0.01303876 0.983 NA 7542 7795 14010.80
## 4: 19 1 339.9426 -0.01479215 0.964 NA 9694 9949 16170.46
## 5: 20 1 366.4635 -0.01589229 0.980 NA 10234 10489 16710.46
## 6: 21 1 369.0801 -0.01554835 0.989 NA 10774 11029 17250.46
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11565.35 100.47 96.779 -0.01464672 3.143711e-04 -0.01496109 -0.01496109
## 2: 13725.16 100.59 97.116 -0.01392057 -2.680703e-05 -0.01389376 -0.01389376
## 3: 14265.87 100.58 97.245 -0.01303876 -1.121969e-04 -0.01292656 -0.01292656
## 4: 16425.46 100.37 96.595 -0.01479215 -4.533309e-04 -0.01433882 -0.01433882
## 5: 16965.46 100.53 96.483 -0.01589229 -5.386292e-04 -0.01535366 -0.01535366
## 6: 17505.46 100.56 96.792 -0.01554835 -6.239275e-04 -0.01492442 -0.01492442
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1610103
## 2: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1495238
## 3: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1391148
## 4: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1543134
## 5: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1652351
## 6: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.1606156
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -372.8818 NA mgO2/hr/kg -372.8818
## 2: -346.2802 NA mgO2/hr/kg -346.2802
## 3: -322.1743 NA mgO2/hr/kg -322.1743
## 4: -357.3724 NA mgO2/hr/kg -357.3724
## 5: -382.6659 NA mgO2/hr/kg -382.6659
## 6: -371.9677 NA mgO2/hr/kg -371.9677
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 1 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004318 | ch4 | Dell | 0.0486 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 366.2336 | 0.1581397 | 0.9724 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.35
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 15 16 23 24 34 35 41 42 45 47 48 49 50 51 57 58 75 76 81 82
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.08
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 354.6940 -0.05393352 0.9958415 NA 27 87
## 2: NA 2 354.4349 -0.05387930 0.9957958 NA 26 86
## 3: NA 3 354.3406 -0.05385955 0.9959755 NA 24 84
## 4: NA 4 354.3147 -0.05385368 0.9959872 NA 28 88
## 5: NA 5 354.2864 -0.05384823 0.9959764 NA 25 85
## ---
## 237: NA 237 220.7518 -0.02616230 0.9639190 NA 129 189
## 238: NA 238 220.3942 -0.02608990 0.9642217 NA 132 192
## 239: NA 239 220.2687 -0.02606341 0.9642537 NA 130 190
## 240: NA 240 220.1843 -0.02604644 0.9642938 NA 131 191
## 241: NA 241 220.0655 -0.02602274 0.9643926 NA 133 193
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 4730.38 4790.38 99.522 96.346 -0.05393352
## 2: 4729.42 4789.42 99.623 96.395 -0.05387930
## 3: 4727.35 4787.35 99.740 96.439 -0.05385955
## 4: 4731.35 4791.35 99.468 96.330 -0.05385368
## 5: 4728.35 4788.35 99.642 96.436 -0.05384823
## ---
## 237: 4832.35 4892.35 94.422 92.726 -0.02616230
## 238: 4835.35 4895.35 94.317 92.615 -0.02608990
## 239: 4833.35 4893.35 94.355 92.694 -0.02606341
## 240: 4834.35 4894.35 94.301 92.654 -0.02604644
## 241: 4836.35 4896.35 94.170 92.612 -0.02602274
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.05393352
## Adjustment : 0.00110541
## Adjusted Rate : -0.05503893
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 241 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 240 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 354.694 -0.05393352 0.9958415 NA 27 87 4730.38
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4790.38 99.522 96.346 -0.05393352 0.00110541 -0.05503893 -0.05503893
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0004318 NA 37 30 1.013253 -0.5923254
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1371.759 NA mgO2/hr/kg -1371.759
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 1 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004318 | ch4 | Dell | 0.0486 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 366.2336 | 0.1581397 | 0.9724 | 1371.759 | 0.5923254 | 0.9958415 | 1005.525 | 0.4341857 |
Exporting data
## Rows: 350 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.2
Enter specimen data
Replicate = 2
mass = 0.0006713
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 June 2023 12 01PM/Oxygen"
experiment_mmr_date2 <- "29 June 2023 12 01PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.001431895post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0007111568Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 8.57
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 3 4 5 6 20 22 47 48 50 51 53 54 55 56 61 62 71 72 74 75
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 8.57
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 3 rate(s) removed, 18 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 12 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 10 1 268.9655 -0.01484514 0.982 NA 4876 5131 11310.35
## 2: 11 1 305.3973 -0.01719899 0.955 NA 5416 5671 11850.35
## 3: 13 1 318.7770 -0.01687659 0.993 NA 6496 6751 12930.35
## 4: 16 1 347.5976 -0.01693584 0.965 NA 8076 8329 14550.60
## 5: 18 1 329.9610 -0.01463624 0.975 NA 9154 9409 15630.46
## 6: 20 1 329.4214 -0.01365596 0.955 NA 10234 10489 16710.46
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11565.35 100.65 96.979 -0.01484514 0.0004613687 -0.01530651 -0.01530651
## 2: 12105.36 100.92 96.846 -0.01719899 0.0003567152 -0.01755570 -0.01755570
## 3: 13185.35 100.31 96.046 -0.01687659 0.0001474110 -0.01702400 -0.01702400
## 4: 14805.46 100.73 96.281 -0.01693584 -0.0001665816 -0.01676926 -0.01676926
## 5: 15885.46 100.89 97.245 -0.01463624 -0.0003758732 -0.01426037 -0.01426037
## 6: 16965.46 100.84 97.508 -0.01365596 -0.0005851783 -0.01307078 -0.01307078
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1686933
## 2: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1934817
## 3: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1876218
## 4: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1848143
## 5: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1571638
## 6: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.1440533
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -251.2935 NA mgO2/hr/kg -251.2935
## 2: -288.2195 NA mgO2/hr/kg -288.2195
## 3: -279.4902 NA mgO2/hr/kg -279.4902
## 4: -275.3081 NA mgO2/hr/kg -275.3081
## 5: -234.1186 NA mgO2/hr/kg -234.1186
## 6: -214.5885 NA mgO2/hr/kg -214.5885
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 2 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0006713 | ch3 | Dell7440 | 0.04977 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 265.686 | 0.178355 | 0.974 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.35
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 7 8 10 14 15 18 19 32 33 34 35 37 38 70 71
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.96 1.12
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 383.8782 -0.04879716 0.9925113 NA 180 240
## 2: NA 2 383.6389 -0.04875772 0.9927326 NA 179 239
## 3: NA 3 383.2800 -0.04869728 0.9927604 NA 181 241
## 4: NA 4 382.8682 -0.04862910 0.9928051 NA 184 244
## 5: NA 5 382.6918 -0.04859973 0.9927924 NA 183 243
## ---
## 237: NA 237 301.5358 -0.03514008 0.9856579 NA 237 297
## 238: NA 238 301.3851 -0.03511459 0.9861090 NA 240 300
## 239: NA 239 301.3588 -0.03511068 0.9861228 NA 241 301
## 240: NA 240 300.9969 -0.03505095 0.9849922 NA 238 298
## 241: NA 241 300.7900 -0.03501674 0.9856623 NA 239 299
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 5968.38 6028.38 92.648 89.780 -0.04879716
## 2: 5967.36 6027.36 92.583 89.631 -0.04875772
## 3: 5969.35 6029.35 92.618 89.726 -0.04869728
## 4: 5972.35 6032.35 92.520 89.517 -0.04862910
## 5: 5971.35 6031.35 92.472 89.496 -0.04859973
## ---
## 237: 6025.35 6085.35 89.857 87.741 -0.03514008
## 238: 6028.35 6088.35 89.780 87.638 -0.03511459
## 239: 6029.35 6089.35 89.726 87.481 -0.03511068
## 240: 6026.36 6086.36 89.715 87.715 -0.03505095
## 241: 6027.35 6087.35 89.631 87.651 -0.03501674
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.04879716
## Adjustment : 0.001431895
## Adjusted Rate : -0.05022905
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 241 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 240 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 383.8782 -0.04879716 0.9925113 NA 180 240 5968.38
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6028.38 92.648 89.78 -0.04879716 0.001431895 -0.05022905 -0.05022905
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0006713 NA 37 30 1.013253 -0.5535754
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -824.6319 NA mgO2/hr/kg -824.6319
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 2 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0006713 | ch3 | Dell7440 | 0.04977 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 265.686 | 0.178355 | 0.974 | 824.6319 | 0.5535754 | 0.9925113 | 558.9459 | 0.3752204 |
Exporting data
## Rows: 351 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3
Enter specimen data
Replicate = 3
mass = 0.0005840
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 June 2023 12 01PM/Oxygen"
experiment_mmr_date2 <- "29 June 2023 12 01PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.001374391post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001942909Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 8.57
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 3 4 5 6 20 22 47 48 50 51 53 54 55 56 61 62 71 72 74 75
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 8.57
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 3 1 227.4645 -0.01670619 0.985 NA 1126 1381 7560.35
## 2: 14 1 363.5929 -0.01939631 0.968 NA 7066 7316 13500.35
## 3: 15 1 377.3485 -0.01961507 0.980 NA 7572 7825 14040.81
## 4: 17 1 389.5378 -0.01903491 0.988 NA 8644 8899 15120.46
## 5: 18 1 359.6130 -0.01647768 0.972 NA 9184 9439 15660.46
## 6: 19 1 439.5438 -0.02085810 0.986 NA 9724 9979 16200.46
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7815.35 100.90 96.543 -0.01670619 0.0009970478 -0.01770324 -0.01770324
## 2: 13755.16 101.19 96.099 -0.01939631 -0.0007848703 -0.01861144 -0.01861144
## 3: 14295.87 101.60 97.050 -0.01961507 -0.0009470410 -0.01866803 -0.01866803
## 4: 15375.46 101.59 96.443 -0.01903491 -0.0012709173 -0.01776399 -0.01776399
## 5: 15915.46 101.62 97.500 -0.01647768 -0.0014329124 -0.01504477 -0.01504477
## 6: 16455.46 101.28 95.922 -0.02085810 -0.0015949076 -0.01926319 -0.01926319
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1800542
## 2: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1892912
## 3: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1898668
## 4: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1806721
## 5: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1530157
## 6: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.1959200
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -308.3121 NA mgO2/hr/kg -308.3121
## 2: -324.1288 NA mgO2/hr/kg -324.1288
## 3: -325.1144 NA mgO2/hr/kg -325.1144
## 4: -309.3700 NA mgO2/hr/kg -309.3700
## 5: -262.0132 NA mgO2/hr/kg -262.0132
## 6: -335.4795 NA mgO2/hr/kg -335.4795
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 3 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.000584 | ch2 | Dell7440 | 0.04593 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 320.481 | 0.1871609 | 0.9814 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.35
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 7 8 10 14 15 18 19 32 33 34 35 37 38 70 71
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.96 1.12
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 412.6118 -0.05312229 0.9871950 NA 164 224
## 2: NA 2 411.6109 -0.05295519 0.9869631 NA 163 223
## 3: NA 3 411.4617 -0.05292966 0.9867901 NA 165 225
## 4: NA 4 411.3502 -0.05291152 0.9863006 NA 162 222
## 5: NA 5 411.1088 -0.05287204 0.9868707 NA 160 220
## ---
## 237: NA 237 251.6954 -0.02613041 0.9042194 NA 126 186
## 238: NA 238 250.7176 -0.02596441 0.9067073 NA 125 185
## 239: NA 239 249.4532 -0.02574795 0.9109527 NA 122 182
## 240: NA 240 249.0769 -0.02568681 0.9111366 NA 124 184
## 241: NA 241 248.2690 -0.02554964 0.9127557 NA 123 183
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 5952.35 6012.35 96.395 93.212 -0.05312229
## 2: 5951.35 6011.35 96.367 93.253 -0.05295519
## 3: 5953.35 6013.35 96.164 93.304 -0.05292966
## 4: 5950.36 6010.36 96.456 93.365 -0.05291152
## 5: 5948.35 6008.35 96.611 93.416 -0.05287204
## ---
## 237: 5914.35 5974.35 97.314 95.267 -0.02613041
## 238: 5913.35 5973.35 97.399 95.270 -0.02596441
## 239: 5910.35 5970.35 97.515 95.666 -0.02574795
## 240: 5912.35 5972.35 97.396 95.410 -0.02568681
## 241: 5911.35 5971.35 97.410 95.579 -0.02554964
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.05312229
## Adjustment : 0.001374391
## Adjusted Rate : -0.05449668
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 41 rate(s) removed, 200 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 199 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 412.6118 -0.05312229 0.987195 NA 164 224 5952.35
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6012.35 96.395 93.212 -0.05312229 0.001374391 -0.05449668 -0.05449668
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.000584 NA 37 30 1.013253 -0.5542691
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -949.0909 NA mgO2/hr/kg -949.0909
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 3 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.000584 | ch2 | Dell7440 | 0.04593 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 320.481 | 0.1871609 | 0.9814 | 949.0909 | 0.5542691 | 0.987195 | 628.6099 | 0.3671082 |
Exporting data
## Rows: 352 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4
Enter specimen data
Replicate = 4
mass = 0.0005225
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 June 2023 12 12PM/Oxygen"
experiment_mmr_date2 <- "29 June 2023 12 12PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.001905599post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001768897Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 8.57
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 3 4 5 6 20 22 47 48 50 51 53 54 55 56 61 62 71 72 74 75
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 8.57
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 2 1 252.5356 -0.02183169 0.974 NA 556 811 6990.35
## 2: 10 1 402.0431 -0.02654448 0.972 NA 4876 5131 11310.35
## 3: 15 1 468.0005 -0.02619731 0.996 NA 7542 7795 14010.80
## 4: 18 1 536.1485 -0.02784907 0.997 NA 9154 9409 15630.46
## 5: 20 1 530.4281 -0.02570043 0.997 NA 10234 10489 16710.46
## 6: 21 1 589.8320 -0.02831299 0.995 NA 10774 11029 17250.46
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7245.35 99.776 93.836 -0.02183169 0.0016770314 -0.02350873 -0.02350873
## 2: 11565.35 101.060 94.536 -0.02654448 0.0002415254 -0.02678600 -0.02678600
## 3: 14265.87 100.900 94.071 -0.02619731 -0.0006558270 -0.02554148 -0.02554148
## 4: 15885.46 100.590 93.637 -0.02784907 -0.0011940172 -0.02665506 -0.02665506
## 5: 16965.46 100.690 94.248 -0.02570043 -0.0015528937 -0.02414754 -0.02414754
## 6: 17505.46 100.970 94.084 -0.02831299 -0.0017323319 -0.02658066 -0.02658066
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2420673
## 2: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2758132
## 3: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2629984
## 4: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2744648
## 5: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2486451
## 6: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.2736988
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -463.2868 NA mgO2/hr/kg -463.2868
## 2: -527.8721 NA mgO2/hr/kg -527.8721
## 3: -503.3463 NA mgO2/hr/kg -503.3463
## 4: -525.2915 NA mgO2/hr/kg -525.2915
## 5: -475.8759 NA mgO2/hr/kg -475.8759
## 6: -523.8254 NA mgO2/hr/kg -523.8254
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 4 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0005225 | ch1 | Dell7440 | 0.0465 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 511.2422 | 0.2671241 | 0.9914 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 18 19 22 23 24 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 8.57
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 3 4 5 6 20 22 47 48 50 51 53 54 55 56 61 62 71 72 74 75
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.96 1.06
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 545.5176 -0.06889828 0.9945145 NA 76 136
## 2: NA 2 544.5861 -0.06875517 0.9942221 NA 77 137
## 3: NA 3 544.2733 -0.06870880 0.9940715 NA 75 135
## 4: NA 4 542.5557 -0.06844656 0.9937161 NA 74 134
## 5: NA 5 541.4767 -0.06827894 0.9929253 NA 78 138
## ---
## 236: NA 236 378.7515 -0.04327894 0.9735820 NA 4 64
## 237: NA 237 378.6438 -0.04326065 0.9732257 NA 1 61
## 238: NA 238 376.6553 -0.04322536 0.9788628 NA 160 220
## 239: NA 239 377.1276 -0.04302751 0.9738787 NA 3 63
## 240: NA 240 375.6190 -0.04279386 0.9728781 NA 2 62
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 6510.35 6570.35 96.873 92.932 -0.06889828
## 2: 6511.35 6571.35 96.856 92.949 -0.06875517
## 3: 6509.35 6569.35 96.811 92.908 -0.06870880
## 4: 6508.35 6568.35 96.920 92.984 -0.06844656
## 5: 6512.35 6572.35 97.000 93.045 -0.06827894
## ---
## 236: 6438.35 6498.35 99.996 97.366 -0.04327894
## 237: 6435.35 6495.35 100.410 97.615 -0.04326065
## 238: 6594.36 6654.36 91.750 88.956 -0.04322536
## 239: 6437.35 6497.35 100.140 97.425 -0.04302751
## 240: 6436.36 6496.36 100.360 97.555 -0.04279386
##
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 240 adjusted rate(s):
## Rate : -0.06889828
## Adjustment : 0.001905599
## Adjusted Rate : -0.07080388
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 240 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 239 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 545.5176 -0.06889828 0.9945145 NA 76 136 6510.35
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6570.35 96.873 92.932 -0.06889828 0.001905599 -0.07080388 -0.07080388
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0005225 NA 37 30 1.013253 -0.7290615
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1395.333 NA mgO2/hr/kg -1395.333
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 4 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0005225 | ch1 | Dell7440 | 0.0465 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 511.2422 | 0.2671241 | 0.9914 | 1395.333 | 0.7290615 | 0.9945145 | 884.0907 | 0.4619374 |
Exporting data
## Rows: 353 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5
Enter specimen data
Replicate = 5
mass = 0.0004876
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 June 2023 12 39PM/Oxygen"
experiment_mmr_date2_asus <- "29 June 2023 12 39PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -4.04769e-05post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001209104Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 11 12 13 14 15 16 17 19 20 21 22 24
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2_asus$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 4 1 373.2544 -0.02713877 0.966 NA 1210 1399 10106.75
## 2: 5 1 372.6508 -0.02573188 0.996 NA 1610 1799 10646.66
## 3: 16 1 550.8013 -0.02721238 0.985 NA 6013 6202 16587.63
## 4: 19 1 606.6257 -0.02786416 0.982 NA 7214 7403 18207.22
## 5: 20 1 585.0676 -0.02590524 0.976 NA 7615 7804 18747.16
## 6: 21 1 653.6279 -0.02870211 0.988 NA 8016 8206 19286.94
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 10362.07 98.453 91.737 -0.02713877 -0.0006036127 -0.02653516 -0.02653516
## 2: 10901.46 98.573 92.044 -0.02573188 -0.0006802688 -0.02505162 -0.02505162
## 3: 16842.88 99.402 92.534 -0.02721238 -0.0015242025 -0.02568818 -0.02568818
## 4: 18461.74 99.230 92.644 -0.02786416 -0.0017542099 -0.02610995 -0.02610995
## 5: 19001.66 99.270 93.361 -0.02590524 -0.0018309057 -0.02407433 -0.02407433
## 6: 19542.74 99.386 92.259 -0.02870211 -0.0019076726 -0.02679444 -0.02679444
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2815153
## 2: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2657762
## 3: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2725296
## 4: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2770042
## 5: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2554081
## 6: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.2842661
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -577.3488 NA mgO2/hr/kg -577.3488
## 2: -545.0701 NA mgO2/hr/kg -545.0701
## 3: -558.9204 NA mgO2/hr/kg -558.9204
## 4: -568.0971 NA mgO2/hr/kg -568.0971
## 5: -523.8065 NA mgO2/hr/kg -523.8065
## 6: -582.9903 NA mgO2/hr/kg -582.9903
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 5 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004876 | ch4 | Asus | 0.04791 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 566.4853 | 0.2762182 | 0.9834 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.48
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 476.6133 -0.05565147 0.9941830 NA 130 175
## 2: NA 2 476.0388 -0.05556965 0.9939172 NA 129 174
## 3: NA 3 475.9995 -0.05556231 0.9939368 NA 131 176
## 4: NA 4 475.0024 -0.05541798 0.9935483 NA 132 177
## 5: NA 5 474.7668 -0.05538715 0.9934211 NA 128 173
## ---
## 174: NA 174 248.4842 -0.02236388 0.8702473 NA 19 64
## 175: NA 175 246.1340 -0.02201407 0.8658444 NA 15 60
## 176: NA 176 245.9754 -0.02199410 0.8690167 NA 18 63
## 177: NA 177 244.4880 -0.02177321 0.8693261 NA 16 61
## 178: NA 178 244.4664 -0.02177119 0.8692902 NA 17 62
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 6913.05 6973.05 91.760 88.676 -0.05565147
## 2: 6911.70 6971.70 91.828 88.700 -0.05556965
## 3: 6914.44 6974.44 91.751 88.622 -0.05556231
## 4: 6915.81 6975.81 91.694 88.563 -0.05541798
## 5: 6910.37 6970.37 91.859 88.764 -0.05538715
## ---
## 174: 6763.23 6823.23 97.227 95.746 -0.02236388
## 175: 6757.80 6817.80 97.814 95.841 -0.02201407
## 176: 6761.91 6821.91 97.329 95.778 -0.02199410
## 177: 6759.21 6819.21 97.644 95.823 -0.02177321
## 178: 6760.56 6820.56 97.479 95.802 -0.02177119
##
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 178 adjusted rate(s):
## Rate : -0.05565147
## Adjustment : -4.04769e-05
## Adjusted Rate : -0.05561099
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 26 rate(s) removed, 152 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 151 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 476.6133 -0.05565147 0.994183 NA 130 175 6913.05
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6973.05 91.76 88.676 -0.05565147 -4.04769e-05 -0.05561099 -0.05561099
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0004876 NA 37 30 1.013253 -0.589985
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1209.977 NA mgO2/hr/kg -1209.977
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 5 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004876 | ch4 | Asus | 0.04791 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 566.4853 | 0.2762182 | 0.9834 | 1209.977 | 0.589985 | 0.994183 | 643.4921 | 0.3137667 | ||
| ### Expor | ting data |
## Rows: 354 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.7
Enter specimen data
Replicate = 7
mass = 0.0003497
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 June 2023 12 59PM/Oxygen"
experiment_mmr_date2_asus <- "29 June 2023 12 59PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0003542458post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0019847Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 11 12 13 14 15 16 17 19 20 21 22 24
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 4 1 250.5303 -0.01490335 0.989 NA 1210 1391 10106.75
## 2: 12 1 305.3666 -0.01428902 0.991 NA 4413 4594 14426.94
## 3: 14 1 354.0189 -0.01640282 0.991 NA 5212 5394 15506.42
## 4: 16 1 370.1599 -0.01631520 0.993 NA 6013 6194 16587.63
## 5: 18 1 372.1422 -0.01542905 0.992 NA 6813 6995 17667.16
## 6: 20 1 414.8873 -0.01683325 0.995 NA 7615 7797 18747.16
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 10351.23 99.654 96.337 -0.01490335 -0.001138851 -0.01376450 -0.01376450
## 2: 14671.86 99.289 95.385 -0.01428902 -0.001995085 -0.01229393 -0.01229393
## 3: 15752.18 99.562 95.480 -0.01640282 -0.002209103 -0.01419372 -0.01419372
## 4: 16832.08 99.511 95.749 -0.01631520 -0.002423251 -0.01389195 -0.01389195
## 5: 17912.37 99.469 95.569 -0.01542905 -0.002637271 -0.01279178 -0.01279178
## 6: 18992.22 99.254 95.040 -0.01683325 -0.002851294 -0.01398195 -0.01398195
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1393849
## 2: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1244933
## 3: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1437314
## 4: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1406755
## 5: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1295347
## 6: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.1415869
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -398.5841 NA mgO2/hr/kg -398.5841
## 2: -356.0004 NA mgO2/hr/kg -356.0004
## 3: -411.0134 NA mgO2/hr/kg -411.0134
## 4: -402.2749 NA mgO2/hr/kg -402.2749
## 5: -370.4168 NA mgO2/hr/kg -370.4168
## 6: -404.8811 NA mgO2/hr/kg -404.8811
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 7 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0003497 | ch2 | Asus | 0.04573 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 397.4341 | 0.1389827 | 0.992 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 5 6 7 8 11 12 13 14 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.41
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 398.9111 -0.037708554 0.9921018 NA 55 100
## 2: NA 2 398.3712 -0.037641607 0.9918503 NA 54 99
## 3: NA 3 398.0386 -0.037599255 0.9916361 NA 56 101
## 4: NA 4 396.9570 -0.037465640 0.9910418 NA 53 98
## 5: NA 5 396.7796 -0.037441867 0.9910798 NA 57 102
## ---
## 174: NA 174 150.2084 -0.007010519 0.7864835 NA 174 219
## 175: NA 175 145.0502 -0.006379905 0.7656599 NA 175 220
## 176: NA 176 141.3112 -0.005922835 0.7430687 NA 176 221
## 177: NA 177 138.4048 -0.005567575 0.7191215 NA 177 222
## 178: NA 178 135.4639 -0.005208139 0.6915239 NA 178 223
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 7986.25 8046.25 97.698 95.556 -0.037708554
## 2: 7984.92 8044.92 97.699 95.598 -0.037641607
## 3: 7987.64 8047.64 97.679 95.559 -0.037599255
## 4: 7983.58 8043.58 97.708 95.634 -0.037465640
## 5: 7989.01 8049.01 97.655 95.509 -0.037441867
## ---
## 174: 8147.19 8207.19 93.302 92.796 -0.007010519
## 175: 8148.54 8208.54 93.244 92.794 -0.006379905
## 176: 8149.91 8209.91 93.168 92.772 -0.005922835
## 177: 8151.26 8211.26 93.114 92.763 -0.005567575
## 178: 8152.60 8212.60 93.096 92.768 -0.005208139
##
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 178 adjusted rate(s):
## Rate : -0.03770855
## Adjustment : -0.0003542458
## Adjusted Rate : -0.03735431
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 17 rate(s) removed, 161 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 160 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 398.9111 -0.03770855 0.9921018 NA 55 100 7986.25
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 8046.25 97.698 95.556 -0.03770855 -0.0003542458 -0.03735431 -0.03735431
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0003497 NA 37 30 1.013253 -0.3782649
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1081.684 NA mgO2/hr/kg -1081.684
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 7 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0003497 | ch2 | Asus | 0.04573 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 397.4341 | 0.1389827 | 0.992 | 1081.684 | 0.3782649 | 0.9921018 | 684.2499 | 0.2392822 | ||
| ### Expor | ting data |
## Rows: 355 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.8
Enter specimen data
Replicate = 8
mass = 0.0004363
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 June 2023 01 08PM/Oxygen"
experiment_mmr_date2_asus <- "29 June 2023 01 08PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0001158766post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0020101Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 11 12 13 14 15 16 17 19 20 21 22 24
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 298.1241 -0.01546779 0.996 NA 3212 3394 12807.22
## 2: 10 1 298.6105 -0.01490689 0.953 NA 3612 3794 13346.69
## 3: 12 1 301.0500 -0.01396336 0.991 NA 4413 4594 14426.94
## 4: 15 1 353.4306 -0.01582176 0.970 NA 5613 5795 16047.69
## 5: 17 1 368.9603 -0.01573558 0.984 NA 6412 6594 17126.47
## 6: 19 1 389.9165 -0.01596449 0.991 NA 7214 7396 18207.22
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 13052.57 99.934 96.237 -0.01546779 -0.001649283 -0.01381851 -0.01381851
## 2: 13592.24 99.838 96.307 -0.01490689 -0.001773516 -0.01313338 -0.01313338
## 3: 14671.86 99.461 96.292 -0.01396336 -0.002022165 -0.01194120 -0.01194120
## 4: 16293.21 99.857 95.116 -0.01582176 -0.002395403 -0.01342636 -0.01342636
## 5: 17371.82 99.545 95.752 -0.01573558 -0.002643767 -0.01309181 -0.01309181
## 6: 18452.26 99.277 95.534 -0.01596449 -0.002892569 -0.01307193 -0.01307193
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1396870
## 2: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1327613
## 3: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1207099
## 4: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1357229
## 5: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1323411
## 6: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.1321401
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -320.1628 NA mgO2/hr/kg -320.1628
## 2: -304.2889 NA mgO2/hr/kg -304.2889
## 3: -276.6671 NA mgO2/hr/kg -276.6671
## 4: -311.0771 NA mgO2/hr/kg -311.0771
## 5: -303.3259 NA mgO2/hr/kg -303.3259
## 6: -302.8651 NA mgO2/hr/kg -302.8651
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 8 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004363 | ch1 | Asus | 0.04565 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 308.344 | 0.1345305 | 0.9788 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.53
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.44
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 772.4076 -0.077997105 0.9735665 NA 163 208
## 2: NA 2 770.4239 -0.077767173 0.9722382 NA 164 209
## 3: NA 3 770.0767 -0.077732179 0.9721510 NA 162 207
## 4: NA 4 766.3028 -0.077292522 0.9696738 NA 165 210
## 5: NA 5 762.4786 -0.076863451 0.9669744 NA 161 206
## ---
## 173: NA 173 127.9761 -0.003629088 0.1672702 NA 37 82
## 174: NA 174 127.8752 -0.003619856 0.1743150 NA 41 86
## 175: NA 175 125.3957 -0.003328179 0.1504622 NA 38 83
## 176: NA 176 124.2384 -0.003194618 0.1435938 NA 40 85
## 177: NA 177 123.8118 -0.003143937 0.1407204 NA 39 84
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 8692.27 8752.27 93.974 90.145 -0.077997105
## 2: 8693.61 8753.61 93.963 90.214 -0.077767173
## 3: 8690.91 8750.91 93.856 90.157 -0.077732179
## 4: 8694.95 8754.95 93.968 90.154 -0.077292522
## 5: 8689.54 8749.54 93.850 90.167 -0.076863451
## ---
## 173: 8521.82 8581.82 97.198 96.821 -0.003629088
## 174: 8527.21 8587.21 96.951 96.658 -0.003619856
## 175: 8523.20 8583.20 97.261 96.745 -0.003328179
## 176: 8525.88 8585.88 97.152 96.646 -0.003194618
## 177: 8524.54 8584.54 97.242 96.687 -0.003143937
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.07799711
## Adjustment : -0.0001158766
## Adjusted Rate : -0.07788123
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 118 rate(s) removed, 59 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 58 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 772.4076 -0.07799711 0.9735665 NA 163 208 8692.27
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 8752.27 93.974 90.145 -0.07799711 -0.0001158766 -0.07788123 -0.07788123
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0004363 NA 37 30 1.013253 -0.7872773
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1804.44 NA mgO2/hr/kg -1804.44
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 8 | CPRE447 | CPRE452 | Pretty patches | 152 | 0.0004363 | ch1 | Asus | 0.04565 | 2023-06-29 | 2024-08-16 | good/good | 37 | 30 | 308.344 | 0.1345305 | 0.9788 | 1804.44 | 0.7872773 | 0.9735665 | 1496.096 | 0.6527468 | ||
| ### Expor | ting data |
## Rows: 356 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.